Hello, and welcome back. So today, we have another interactive online app that we're going to be showing. Here it is, where we are primarily going to be looking at boxplots and histograms of our data. Now, you can upload your own data using this Upload User Data, first browsing and then uploading, but for now we're just going to look at the Default Dataset which looks at car's data. So, this is a couple of dozen cars that we got a lot of data on, such as miles per gallon, the weight of the car, the displacement, the number of cylinders, and in such, and we're going to look at the numerical summaries, the boxplot of our data, and the histogram of our data. So, right now, I've clicked the Use Default Dataset, and I can go to Upload Plots, or Update Plots, and we see I have my summaries. So, my five number summaries along with the mean, the sample size of 32, a standard deviation of 6.07, and below we have a boxplot of right now, miles per gallon, and a histogram of our miles per gallon as well. Now, I can change this to be something more like weight, or WT, and we'll have a description of all of these later on in the app. Clicking Update Plots again, this updates add now to my numerical summaries on weight, and my boxplot, and my histogram again. So, to go back to miles per gallon, one of the really nice features about this is that I'm able to facet my data, so this is taking a categorical variable, and comparing between them. So, what I could choose here is cylinders, so the number of cylinders our cars have, which is either four, six, or eight. Then by updating plots, I'll now get three boxplots, three histograms, and three different numerical summaries. So, we can see now that in the boxplots, four cylinder cars, tend to have better miles per gallon, than do six, and six cylinders have better miles per gallon than do, eight cylinder cars. The histogram also shows this down here, and we can change the bin size if we wanted to. So, I could make my bins have fewer bins, and then they'd become a little bit wider, and I'll always be clicking this Update Plots on the bottom left here. We could also change it up, and instead of looking at miles per gallon, I could look at the horsepower of a car. Still faceting by weight, clicking the Update Plots, we now see a reverse trend, where the more horsepower we have, means the more cylinders the car would have. So, the eight cylinders have the highest horsepower, whereas the four cylinders have pretty low horsepower. The histograms again, are down there, with our numerical summaries up top. One of the cool features about this app, is that we can upload our own data. So what I'm going to do is click Browse, and I'm going to find what is called the Iris Dataset, which is up here in my folder. Now it's uploaded, I'm going to use Upload User Data, and it reads on that data, reads in your categorical variables that reads in the numerical variables. Now, I can see that I have these variables here, Sepal.Length, Sepal.Width, Petal.Length, and Petal.Width. So, the Iris Dataset is based off of flowers, that there are three species of flowers that look often similar, but they have different properties. So, let's just do a basic Sepal.Length across all of our a 150 flowers, because my sample size is 150 and I got our boxplot and our histogram. But now, instead, I'm going to facet it by species. So, again, there's three species, setosa, versa color, and virginica, and we can see here that they actually differ quite a bit, and so that's how we're able to determine what kind of flower you would actually have. I could change my variable to be something like Petal Length, and see how those differ. So the setosa has very very small Petal Length, whereas the versa color and virginica have quite a lot larger length. So, what's nice about this is that you can upload your own data set into it, and use it just to get an instant feel with your numerical summaries, and your boxplots on the data that you have. You can even facet it by the categorical variables that you have.